International Journal of Computer Techniques Volume 12 Issue 4 | The Power of Personalized Finance: Harnessing the Potential of Machine Learning in Hyper-Personalized Banking
The Power of Personalized Finance: Harnessing the Potential of Machine Learning in Hyper-Personalized Banking
Author: Sundaravaradan Ravathanallur Chackrvarti
Atlanta, Georgia, USA
Email: ramkrishhare@gmail.com
ORCID ID: 0009-0006-5994-5174
Journal: International Journal of Computer Techniques (IJCT)
Volume: 12 | Issue: 4 | Publication Date: July – August 2025
ISSN: 2394-2231 | Journal URL: https://ijctjournal.org/
Abstract
This paper investigates the transformative role of machine learning in personalized finance, focusing on hyper-personalized banking applications. It analyzes ML’s impact on credit scoring, fraud detection, and risk-based pricing, supported by a six-stage maturity framework for institutional adoption. Results show significant improvements in credit access (45%), fraud detection accuracy (92%), and operational efficiency (80%). Ethical governance and explainable AI are emphasized as critical enablers of trust and inclusion.
Keywords
Machine Learning, Personalized Finance, Credit Scoring, Fintech, Fraud Detection, Risk-Based Pricing, MLOps, AI Governance, Explainable AI, Financial Inclusion
Conclusion
ML-driven personalized finance enhances customer experience, operational efficiency, and financial inclusion. The proposed maturity framework guides institutions in ethical AI adoption. Responsible use of behavioral data, transparency, and fairness are essential for building trust in digital banking ecosystems.
References
Includes 15 references from Nature Medicine, IEEE, World Bank, McKinsey, and regulatory bodies covering AI in finance, credit scoring, and ethical governance.
